Life Distribution Models and Incomplete Data.

Abstract

This report represents the second chapter of a book in preparation on inference and data analysis in reliability and life testing. The point of view adopted differs from that of most books on the subject in the following basic respect: Prior information available to the reliability analyst is utilized fully in a formal statistical fashion. Experience accumulated in helping engineers, quality assurance managers, scientists, biostatisticians, and others who must make estimates and reach decisions from either planned experiments or retrospective data has shown us that the point of view adopted throughout the book has resulted in useful solutions to real-life problems. By contrast, more classical statistical methods have often proven inadequate in many practical problems simply because the data available are insufficient to reach conclusions with a desired degree of assurance. The book is intended primarily for actual use by the engineering and scientific practitioner, rather than for theoretical study and philosophical analysis by the statistician. Thus we omit a philosophical justification of the methods presented; rather, we rely on the fact that they have led to useful answers to problems that have arisen in practice.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1980
Accession Number
ADA095068

Entities

People

  • Frank Proschan
  • Richard E. Barlow

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Aircrafts
  • Airplanes
  • California
  • Data Analysis
  • Data Science
  • Engineering
  • Estimators
  • Failure Mode And Effect Analysis
  • Life Tests
  • Operations Research
  • Pressure Vessels
  • Probability
  • Random Variables
  • Spacecraft
  • Time Intervals

Fields of Study

  • Mathematics

Readers

  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms